4 citations · 4 across the 11 of their papers we have counts for
13 papers
NPBoost: Neural Processes with Gradient-Boosted Fixed Effects
Andrea Nava, Ken Rölli, Armin Begic +1
Neural Processes (NPs) are model-based meta-learners that implicitly learn a stochastic process and adapt to a new task from a small context set. Most extensions of NPs focus on im…
Spectrally Deconfounded Gradient Boosting
Andrea Nava, Peter Bühlmann, Fabio Sigrist
Flexible machine-learning methods can be sensitive to hidden confounding: they may learn associations induced by unobserved confounders rather than stable signals. Spectral deconfo…
A Censored Transformed Model for Proportional Outcomes with Boundary Mass and an Application to Loss Given Default Modeling
Yuan Christopher Qiang, Fabio Sigrist
We introduce the zero-one censored transformed normal (ZOC-TN) model for proportional responses with potential probability mass at the boundaries 0 and 1. The model combines a cens…
A new framework for non-stationary spatio-temporal data fusion of multi-fidelity models
Pietro Colombo, Fabio Sigrist, Claire Miller +3
We propose a new scalable framework for spatio-temporal data fusion with multi-fidelity Gaussian processes (MFGPs) that enables fully likelihood-based inference for both stationary…
Laplace Approximations for Mixed-Effects and Gaussian Process Quantile Regression
Andrea Nava, Fabio Sigrist
Laplace approximations are a standard tool for computationally efficient inference in latent Gaussian models, but they fail for quantile regression with the asymmetric Laplace like…
Selecting Hyperparameters for Tree-Boosting
Floris Jan Koster, Fabio Sigrist
Tree-boosting is a widely used machine learning technique for tabular data. However, its out-of-sample accuracy is critically dependent on multiple hyperparameters. In this article…
Scalable non-separable spatio-temporal Gaussian process models for large-scale short-term weather prediction
Tim Gyger, Reinhard Furrer, Fabio Sigrist
Monitoring daily weather fields is critical for climate science, agriculture, and environmental planning, yet fully probabilistic spatio-temporal models become computationally proh…
Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes
Tim Gyger, Reinhard Furrer, Fabio Sigrist
Gaussian processes are flexible, probabilistic, non-parametric models widely used in machine learning and statistics. However, their scalability to large data sets is limited by co…
Scalable Krylov Subspace Methods for Generalized Mixed-Effects Models with Crossed Random Effects
Pascal Kündig, Fabio Sigrist
Mixed-effects models are widely used to model data with complex grouping structures and high-cardinality categorical predictor variables. However, for high-dimensional crossed rand…
An accuracy-runtime trade-off comparison of scalable Gaussian process approximations for spatial data
Filippo Rambelli, Fabio Sigrist
Gaussian processes (GPs) are flexible, probabilistic, nonparametric models widely used in fields such as spatial statistics and machine learning. A drawback of Gaussian processes i…
A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios
Pascal Kündig, Fabio Sigrist
We introduce a novel machine learning model for credit risk by combining tree-boosting with a latent spatio-temporal Gaussian process model accounting for frailty correlation. This…
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
Tim Gyger, Reinhard Furrer, Fabio Sigrist
Gaussian processes are flexible probabilistic regression models which are widely used in statistics and machine learning. However, a drawback is their limited scalability to large…
Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models
Pascal Kündig, Fabio Sigrist
Latent Gaussian process (GP) models are flexible probabilistic non-parametric function models. Vecchia approximations are accurate approximations for GPs to overcome computational…